arXiv subjects

Dong Liu

Publications and source records attributed to Dong Liu.

At least 145 records · Page 8Linked to original sources

arXiv2025

Quasinormal Modes and Greybody Factors of Scalar Field Perturbations in the NED Corrected Charged Black Hole Spacetime

Inspired by the quark-antiquark confinement potential, Mazharimousavi et al. \cite{Mazharimousavi:2023okd} proposed a nonlinear electrodynamics (NED) model, and based on this model, they constructed a charged black hole solution that includes a logarithmic correction term ($\propto \frac{ζ\ln r}{r}$). On the basis of the Reissner-Nordström metric, this solution realizes a long-range confinement correction by introducing the NED parameter $ζ$, providing a new theoretical perspective for explaining the anomalies in galaxy rotation curves. To deeply explore the dynamic properties of this black hole solution, this paper combines two complementary methods, namely, time-domain evolution and the WKB approximation, to calculate the quasinormal mode (QNM) spectrum of its scalar field perturbations. The research results show that the oscillation frequencies and decay rates of the low-order QNM modes decrease monotonically with the increase of the NED parameter $ζ$, and exhibit an approximately linear dependence. The analysis of the greybody factor (GF) indicates that as $ζ$ increases, the transmittance of the low-frequency scalar field also increases. The enhanced long-range confinement effect caused by the increase of $ζ$ makes low-frequency perturbations more likely to survive and propagate in space-time on the one hand, and at the same time enhances the transmission ability of the low-frequency scalar field. These characteristics provide key theoretical predictions and potential observational features for testing and constraining such NED models in a strong gravitational field environment in the future using the observational data of gravitational wave astronomy or Hawking radiation.

gr-qc↗

arXiv2025

Designing Large Foundation Models for Efficient Training and Inference: A Survey

This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and System Design optimize LLM training and inference from different aspects to save computational resources, making LLMs more efficient, affordable, and more accessible. The paper list repository is available at https://github.com/NoakLiu/Efficient-Foundation-Models-Survey.

cs.DC↗

arXiv2025

SDEIT: Semantic-Driven Electrical Impedance Tomography

Regularization methods using prior knowledge are essential in solving ill-posed inverse problems such as Electrical Impedance Tomography (EIT). However, designing effective regularization and integrating prior information into EIT remains challenging due to the complexity and variability of anatomical structures. In this work, we introduce SDEIT, a novel semantic-driven framework that integrates Stable Diffusion 3.5 into EIT, marking the first use of large-scale text-to-image generation models in EIT. SDEIT employs natural language prompts as semantic priors to guide the reconstruction process. By coupling an implicit neural representation (INR) network with a plug-and-play optimization scheme that leverages SD-generated images as generative priors, SDEIT improves structural consistency and recovers fine details. Importantly, this method does not rely on paired training datasets, increasing its adaptability to varied EIT scenarios. Extensive experiments on both simulated and experimental data demonstrate that SDEIT outperforms state-of-the-art techniques, offering superior accuracy and robustness. This work opens a new pathway for integrating multimodal priors into ill-posed inverse problems like EIT.

cs.CV↗

arXiv2025

Prediction and Reference Quality Adaptation for Learned Video Compression

Temporal prediction is one of the most important technologies for video compression. Various prediction coding modes are designed in traditional video codecs. Traditional video codecs will adaptively to decide the optimal coding mode according to the prediction quality and reference quality. Recently, learned video codecs have made great progress. However, they did not effectively address the problem of prediction and reference quality adaptation, which limits the effective utilization of temporal prediction and reduction of reconstruction error propagation. Therefore, in this paper, we first propose a confidence-based prediction quality adaptation (PQA) module to provide explicit discrimination for the spatial and channel-wise prediction quality difference. With this module, the prediction with low quality will be suppressed and that with high quality will be enhanced. The codec can adaptively decide which spatial or channel location of predictions to use. Then, we further propose a reference quality adaptation (RQA) module and an associated repeat-long training strategy to provide dynamic spatially variant filters for diverse reference qualities. With these filters, our codec can adapt to different reference qualities, making it easier to achieve the target reconstruction quality and reduce the reconstruction error propagation. Experimental results verify that our proposed modules can effectively help our codec achieve a higher compression performance.

eess.IV↗

arXiv2025

Voxel-based Point Cloud Geometry Compression with Space-to-Channel Context

Voxel-based methods are among the most efficient for point cloud geometry compression, particularly with dense point clouds. However, they face limitations due to a restricted receptive field, especially when handling high-bit depth point clouds. To overcome this issue, we introduce a stage-wise Space-to-Channel (S2C) context model for both dense point clouds and low-level sparse point clouds. This model utilizes a channel-wise autoregressive strategy to effectively integrate neighborhood information at a coarse resolution. For high-level sparse point clouds, we further propose a level-wise S2C context model that addresses resolution limitations by incorporating Geometry Residual Coding (GRC) for consistent-resolution cross-level prediction. Additionally, we use the spherical coordinate system for its compact representation and enhance our GRC approach with a Residual Probability Approximation (RPA) module, which features a large kernel size. Experimental results show that our S2C context model not only achieves bit savings while maintaining or improving reconstruction quality but also reduces computational complexity compared to state-of-the-art voxel-based compression methods.

cs.CV↗

arXiv2025

Exploiting Diffusion Prior for Real-World Image Dehazing with Unpaired Training

Unpaired training has been verified as one of the most effective paradigms for real scene dehazing by learning from unpaired real-world hazy and clear images. Although numerous studies have been proposed, current methods demonstrate limited generalization for various real scenes due to limited feature representation and insufficient use of real-world prior. Inspired by the strong generative capabilities of diffusion models in producing both hazy and clear images, we exploit diffusion prior for real-world image dehazing, and propose an unpaired framework named Diff-Dehazer. Specifically, we leverage diffusion prior as bijective mapping learners within the CycleGAN, a classic unpaired learning framework. Considering that physical priors contain pivotal statistics information of real-world data, we further excavate real-world knowledge by integrating physical priors into our framework. Furthermore, we introduce a new perspective for adequately leveraging the representation ability of diffusion models by removing degradation in image and text modalities, so as to improve the dehazing effect. Extensive experiments on multiple real-world datasets demonstrate the superior performance of our method. Our code https://github.com/ywxjm/Diff-Dehazer.

cs.CV↗

arXiv2025

IVCA: Inter-Relation-Aware Video Complexity Analyzer

To address the real-time analysis requirements of video streaming applications, we propose an innovative inter-relation-aware video complexity analyzer (IVCA) to enhance the existing video complexity analyzer (VCA). The IVCA overcomes the limitations of the VCA by incorporating inter-frame relations, focusing on inter motion and reference structure. To begin with, we improve the accuracy of temporal features by integrating feature-domain motion estimation into the IVCA framework, which allows for a more nuanced understanding of motion across frames. Furthermore, inspired by the hierarchical reference structures utilized in modern codecs, we introduce layer-aware weights that effectively adjust the contributions of frame complexity across different layers, ensuring a more balanced representation of video characteristics. In addition, we broaden the analysis of temporal features by considering reference frames rather than relying solely on the preceding frame, thereby enriching the contextual understanding of video content. Experimental results demonstrate a significant enhancement in complexity estimation accuracy achieved by the IVCA, coupled with a negligible increase in time complexity, indicating its potential for real-time applications in video streaming scenarios. This advancement not only improves video processing efficiency but also paves the way for more sophisticated analytical tools in video technology.

eess.IV↗

arXiv2025

USTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s

Image/video coding has been a remarkable research area for both academia and industry for many years. Testing datasets, especially high-quality image/video datasets are desirable for the justified evaluation of coding-related research, practical applications, and standardization activities. We put forward a test dataset namely USTC-TD, which has been successfully adopted in the practical end-to-end image/video coding challenge of the IEEE International Conference on Visual Communications and Image Processing (VCIP) in 2022 and 2023. USTC-TD contains 40 images at 4K spatial resolution and 10 video sequences at 1080p spatial resolution, featuring various content due to the diverse environmental factors (e.g. scene type, texture, motion, view) and the designed imaging factors (e.g. illumination, lens, shadow). We quantitatively evaluate USTC-TD on different image/video features (spatial, temporal, color, lightness), and compare it with the previous image/video test datasets, which verifies its excellent compensation for the shortcomings of existing datasets. We also evaluate both classic standardized and recently learned image/video coding schemes on USTC-TD using objective quality metrics (PSNR, MS-SSIM, VMAF) and subjective quality metric (MOS), providing an extensive benchmark for these evaluated schemes. Based on the characteristics and specific design of the proposed test dataset, we analyze the benchmark performance and shed light on the future research and development of image/video coding. All the data are released online: https://esakak.github.io/USTC-TD.

eess.IV↗

arXiv2025

Penalized Principal Component Analysis for Large-dimension Factor Model with Group Pursuit

This paper investigates the intrinsic group structures within the framework of large-dimensional approximate factor models, which portrays homogeneous effects of the common factors on the individuals that fall into the same group. To this end, we propose a fusion Penalized Principal Component Analysis (PPCA) method and derive a closed-form solution for the $\ell_2$-norm optimization problem. We also show the asymptotic properties of our proposed PPCA estimates. With the PPCA estimates as an initialization, we identify the unknown group structure by a combination of the agglomerative hierarchical clustering algorithm and an information criterion. Then the factor loadings and factor scores are re-estimated conditional on the identified latent groups. Under some regularity conditions, we establish the consistency of the membership estimators as well as that of the group number estimator derived from the information criterion. Theoretically, we show that the post-clustering estimators for the factor loadings and factor scores with group pursuit achieve efficiency gains compared to the estimators by conventional PCA method. Thorough numerical studies validate the established theory and a real financial example illustrates the practical usefulness of the proposed method.

stat.ME↗

arXiv2025

Controllable Antiferromagnetic-Ferromagnetic phase transition in monolayer MnPSe3 via atomic adsorption of Li, O, and F

The engineering of magnetic order and electronic states in two-dimensional (2D) materials is pivotal for advanced spintronic technologies. Despite their potential, the scarcity of intrinsic 2D ferromagnets remains a critical challenge. Here, we employ density functional theory with Hubbard-U corrections to systematically investigate adsorbate-driven magnetic transitions in monolayer MnPSe3. While pristine MnPSe3 exhibits antiferromagnetic (AFM) ordering with a semiconducting gap, Li, O, and F adatom adsorption induces an AFM-to-ferromagnetic (AFM-FM) phase transition across large coverage ranges. Our calculations reveal enhanced thermodynamic stability at elevated coverages, with full-coverage configurations (Li0.5MnPSe3, MnPSe3O0.5, MnPSe3F0.5) favored energetically. Hybrid functional (HSE06) calculations show that F adsorption drives a semiconductor-to-half-metal transition, whereas Li and O adsorption preserves semiconductivity. Moreover, Li adsorption induces a valley splitting of 20.3 meV at the K1 and K2 points in the band structure of the monolayer MnPSe3. Magnetic anisotropy analysis reveals adsorbate-dependent easy-axis reorientation: Li (electron doping) switches the easy-axis from in-plane to out-of-plane, while O/F (hole doping) stabilizes in-plane easy-axis, consistent with carrier-density-modulation simulations. Crucially, carrier doping results indicate that once the electron doping concentration reaches critical concentration, the magnetic easy axis of monolayer MnPSe3 transitions from in-plane to out-of-plane. This work establishes atomic adsorption as a robust strategy for tailoring 2D magnetism, resolving discrepancies through rigorous treatment of exchange-correlation effects and configurational diversity.

cond-mat.mes-hall↗

arXiv2025

Probing black holes via quasinormal modes in a dark energy-induced dark matter

This study delves into the existence of dark matter around supermassive black holes in galactic cores using a novel gravitational model. By analyzing gravitational waves emitted during the ringdown phase of black holes under different field perturbations, we explore the potential for detecting dark matter. The model hypothesizes that the dark matter distribution around black hole is driven by a mechanism where dark energy endows gravitons with mass, thereby forming a new spacetime structure. Results reveal that as relevant parameters increase, the quasinormal modes (QNMs) exhibit a gradual reduction in real parts, with negative imaginary parts whose absolute values also decrease. Moreover, compared to gravitational wave signals from Schwarzschild black hole without dark matter, this system demonstrates significant differences in oscillation modes and frequencies. This achievement not only validates the self-consistency of the new gravitational model but also lays a theoretical foundation for subsequent gravitational wave detection within dark matter. Simultaneously, it provides new theoretical support for understanding the mechanism of dark energy in large-scale cosmic structures and broadens the research perspective on the relationships between black hole physics, dark matter, and dark energy.

gr-qc↗

arXiv2025

TransPCA for Large-dimensional Factor Analysis with Weak Factors: Power Enhancement via Knowledge Transfer

Early work established convergence of the principal component estimators of the factors and loadings up to a rotation for large dimensional approximate factor models with weak factors in that the factor loading $Λ^{(0)}$ scales sublinearly in the number $N$ of cross-section units, i.e., $Λ^{(0)\top}Λ^{(0)}/N^α$ is positive definite in the limit for some $α\in (0,1)$. However, the established convergence rates for weak factors can be much slower especially for small $α$. This article proposes a Transfer Principal Component Analysis (TransPCA) method for enhancing the convergence rates for weak factors by transferring knowledge from large number of available informative panel datasets, which should not be turned a blind eye on in this big data era. We aggregate useful information by analyzing a weighted average projection matrix of the estimated loading spaces from all informative datasets which is highly flexible and computationally efficient. Theoretically, we derive the convergence rates of the estimators of weak/strong loading spaces and factor scores. The results indicate that as long as the auxiliary datasets are similar enough to the target dataset and the auxiliary sample size is sufficiently large, TransPCA estimators can achieve faster convergence rates in contrast to performing PCA solely on the target dataset. To avoid negative transfer, we also investigate the case that the informative datasets are unknown and provide a criterion for selecting useful datasets. Thorough simulation studies and {empirical analysis on real datasets in areas of macroeconomic and finance} are conducted to illustrate the usefulness of our proposed methods where large number of source panel datasets are naturally available.

math.ST↗

arXiv2025

Generalized Gaussian Model for Learned Image Compression

In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The Gaussian model with mean and scale parameters has been widely used for its simplicity and effectiveness. Probabilistic models with more parameters, such as the Gaussian mixture models, can fit the distribution of latent variables more precisely, but the corresponding complexity is higher. To balance the compression performance and complexity, we extend the Gaussian model to the generalized Gaussian family for more flexible latent distribution modeling, introducing only one additional shape parameter beta than the Gaussian model. To enhance the performance of the generalized Gaussian model by alleviating the train-test mismatch, we propose improved training methods, including beta-dependent lower bounds for scale parameters and gradient rectification. Our proposed generalized Gaussian model, coupled with the improved training methods, is demonstrated to outperform the Gaussian and Gaussian mixture models on a variety of learned image compression networks.

eess.IV↗

arXiv2025

Model-Free Privacy Preserving Power Flow Analysis in Distribution Networks

Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and online sharing, we introduce a privacy preserving PF calculation framework, composed of two local strategies: a local randomisation strategy (LRS) and a local zero-knowledge proof (ZKP)-based data collection strategy. First, the LRS is used to achieve irreversible transformation and robust privacy protection for active and reactive power data, thereby ensuring that personal data remains confidential. Subsequently, the ZKP-based data collecting strategy is adopted to securely gather the training dataset for the ANN, enabling SMs to interact with the distribution system operator without revealing the actual voltage magnitude. Moreover, to mitigate the accuracy loss induced by the seasonal variations in load profiles, an incremental learning strategy is incorporated into the online application. The results across three datasets with varying measurement errors demonstrate that the proposed framework efficiently collects one month of SM data within one hour. Furthermore, it robustly maintains mean errors of 0.005 p.u. and 0.014 p.u. under multiple measurement errors and seasonal variations in load profiles, respectively.

eess.SY↗

arXiv2025

Does Table Source Matter? Benchmarking and Improving Multimodal Scientific Table Understanding and Reasoning

Recent large language models (LLMs) have advanced table understanding capabilities but rely on converting tables into text sequences. While multimodal large language models (MLLMs) enable direct visual processing, they face limitations in handling scientific tables due to fixed input image resolutions and insufficient numerical reasoning capabilities. We present a comprehensive framework for multimodal scientific table understanding and reasoning with dynamic input image resolutions. Our framework consists of three key components: (1) MMSci-Pre, a domain-specific table structure learning dataset of 52K scientific table structure recognition samples, (2) MMSci-Ins, an instruction tuning dataset with 12K samples across three table-based tasks, and (3) MMSci-Eval, a benchmark with 3,114 testing samples specifically designed to evaluate numerical reasoning capabilities. Extensive experiments demonstrate that our domain-specific approach with 52K scientific table images achieves superior performance compared to 150K general-domain tables, highlighting the importance of data quality over quantity. Our proposed table-based MLLMs with dynamic input resolutions show significant improvements in both general table understanding and numerical reasoning capabilities, with strong generalisation to held-out datasets. Our code and data are publicly available at https://github.com/Bernard-Yang/MMSci_Table.

cs.CL↗

arXiv2025

Multimodal semantic retrieval for product search

Semantic retrieval (also known as dense retrieval) based on textual data has been extensively studied for both web search and product search application fields, where the relevance of a query and a potential target document is computed by their dense vector representation comparison. Product image is crucial for e-commerce search interactions and is a key factor for customers at product explorations. However, its impact on semantic retrieval has not been well studied yet. In this research, we build a multimodal representation for product items in e-commerce search in contrast to pure-text representation of products, and investigate the impact of such representations. The models are developed and evaluated on e-commerce datasets. We demonstrate that a multimodal representation scheme for a product can show improvement either on purchase recall or relevance accuracy in semantic retrieval. Additionally, we provide numerical analysis for exclusive matches retrieved by a multimodal semantic retrieval model versus a text-only semantic retrieval model, to demonstrate the validation of multimodal solutions.

cs.IR↗

arXiv2025

Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression

In this paper, we study how to synthesize a dynamic reference from an external dictionary to perform conditional coding of the input image in the latent domain and how to learn the conditional latent synthesis and coding modules in an end-to-end manner. Our approach begins by constructing a universal image feature dictionary using a multi-stage approach involving modified spatial pyramid pooling, dimension reduction, and multi-scale feature clustering. For each input image, we learn to synthesize a conditioning latent by selecting and synthesizing relevant features from the dictionary, which significantly enhances the model's capability in capturing and exploring image source correlation. This conditional latent synthesis involves a correlation-based feature matching and alignment strategy, comprising a Conditional Latent Matching (CLM) module and a Conditional Latent Synthesis (CLS) module. The synthesized latent is then used to guide the encoding process, allowing for more efficient compression by exploiting the correlation between the input image and the reference dictionary. According to our theoretical analysis, the proposed conditional latent coding (CLC) method is robust to perturbations in the external dictionary samples and the selected conditioning latent, with an error bound that scales logarithmically with the dictionary size, ensuring stability even with large and diverse dictionaries. Experimental results on benchmark datasets show that our new method improves the coding performance by a large margin (up to 1.2 dB) with a very small overhead of approximately 0.5\% bits per pixel. Our code is publicly available at https://github.com/ydchen0806/CLC.

cs.CV↗

arXiv2025

Hierarchical Multi-field Representations for Two-Stage E-commerce Retrieval

Dense retrieval methods typically target unstructured text data represented as flat strings. However, e-commerce catalogs often include structured information across multiple fields, such as brand, title, and description, which contain important information potential for retrieval systems. We present Cascading Hierarchical Attention Retrieval Model (CHARM), a novel framework designed to encode structured product data into hierarchical field-level representations with progressively finer detail. Utilizing a novel block-triangular attention mechanism, our method captures the interdependencies between product fields in a specified hierarchy, yielding field-level representations and aggregated vectors suitable for fast and efficient retrieval. Combining both representations enables a two-stage retrieval pipeline, in which the aggregated vectors support initial candidate selection, while more expressive field-level representations facilitate precise fine-tuning for downstream ranking. Experiments on publicly available large-scale e-commerce datasets demonstrate that CHARM matches or outperforms state-of-the-art baselines. Our analysis highlights the framework's ability to align different queries with appropriate product fields, enhancing retrieval accuracy and explainability.

cs.IR↗